阅读说明：本技术 一种基于卷积神经网络的车牌校准识别方法、系统及电子设备 (License plate calibration and identification method and system based on convolutional neural network and electronic equipment ) 是由 林初赢 林初煌 于 2020-06-22 设计创作，主要内容包括：本发明公开了一种基于卷积神经网络的车牌校准识别方法、系统及电子设备,包括获取车辆轮廓信息；获取某个车辆的多帧图像,对多帧图像的车牌号进行识别；将相邻帧的车牌号识别结果进行比对,得到多个车牌一致性结果；对多个一致性结果进行检验,确定某个车辆的车牌信息。遇到车辆很快的情况,能够对图像进行清晰化选取,从而得到最优的图像来识别出正确的车牌号,另外,如果连续图像当中的车牌被识别出不同的结果,结合卷积神经网络对车辆的轮廓信息进行联网查询,可以有效避免车牌识别出错。(The invention discloses a license plate calibration identification method, a license plate calibration identification system and electronic equipment based on a convolutional neural network, wherein the license plate calibration identification method comprises the steps of obtaining vehicle contour information; acquiring a multi-frame image of a certain vehicle, and identifying the license plate number of the multi-frame image; comparing license plate number recognition results of adjacent frames to obtain a plurality of license plate consistency results; and (4) checking a plurality of consistency results to determine the license plate information of a certain vehicle. When the vehicle is in a fast condition, the image can be selected clearly, so that the optimal image is obtained to identify the correct license plate number, and in addition, if different results are identified from the license plates in the continuous images, the convolutional neural network is combined to perform networking query on the outline information of the vehicle, so that the license plate identification error can be effectively avoided.)

1. A license plate calibration and identification method based on a convolutional neural network is applied to electronic equipment, and is characterized in that: comprises that

Acquiring vehicle contour information;

acquiring a multi-frame image of a certain vehicle, and identifying the license plate number of the multi-frame image;

comparing license plate number recognition results of adjacent frames to obtain a plurality of license plate consistency results;

and (4) checking a plurality of consistency results to determine the license plate information of a certain vehicle.

2. The convolutional neural network-based license plate calibration identification method of claim 1, wherein: the method comprises the steps of obtaining vehicle contour information, wherein the contour information comprises

And processing the vehicle image by adopting a CNN network to obtain the color, the size and the type information of different vehicles.

3. The convolutional neural network-based license plate calibration identification method of claim 1, wherein: the method for acquiring the multiframe images of a certain vehicle comprises the following steps

The method comprises the steps of extracting a vehicle image needing to be observed, extracting continuous frames of the vehicle image, and sequencing the vehicle image in a multi-frame mode in a time sequence.

4. The convolutional neural network-based license plate calibration identification method of claim 3, wherein: the process of identifying the license plate number of the multi-frame image comprises the following steps:

setting a definition threshold value based on time sequence, removing the images which do not meet the requirements, and identifying the license plate in the images which meet the requirements; wherein the definition threshold can be adaptively adjusted according to human eyes; when the clear threshold is selected, the clear threshold is automatically set for the first time, and the clear threshold can be changed according to manual adjustment;

acquiring license plates in the images meeting the requirements by using a convolutional neural network to obtain a plurality of license plate identification results meeting the requirements;

whether the identification results of the license plates meeting the requirements are consistent or not is checked, if the identification results of the license plates do not meet the requirements, any identification license plate number with the maximum similarity is paired pairwise to define as an adjacent frame;

and if the requirements are met, selecting two frames of images close in time as adjacent frames.

5. The convolutional neural network-based license plate calibration identification method of claim 4, wherein: the method for comparing license plate number recognition results of adjacent frames to obtain a plurality of license plate consistency results comprises the following steps:

identifying license plate numbers of adjacent frame images respectively, identifying inconsistent positions if license plate number results of adjacent frames are inconsistent, and outputting respective results;

defining the symbol at the inconsistent position as a special symbol, marking the special symbol and inputting the special symbol into the networking information;

and inquiring other same symbols except the special symbols in the networking information, and determining final license plate number information by combining the contour information to obtain a license plate consistency result.

6. The utility model provides a license plate calibration identification system based on convolutional neural network, is applied to electronic equipment which characterized in that: comprises that

The vehicle positioning module is used for positioning the vehicle of which the license plate needs to be identified and acquiring a clear outline;

the contour determining module is used for acquiring vehicle contour information;

the image acquisition module and the license plate recognition module are used for acquiring multi-frame images of a certain vehicle and recognizing the license plate number of the multi-frame images;

the result comparison module is used for comparing license plate number identification results of adjacent frames to obtain a plurality of license plate consistency results;

and the result determining module is used for checking a plurality of consistency results and determining the license plate information of a certain vehicle.

7. The convolutional neural network-based license plate calibration recognition system of claim 6, wherein: the contour determination module obtains vehicle contour information, wherein the contour information comprises

And processing the vehicle image by adopting a CNN network to obtain the color, the size and the type information of different vehicles.

8. The convolutional neural network-based license plate calibration recognition system of claim 6, wherein:

the method for acquiring the multi-frame image of a certain vehicle by the image acquisition module comprises the following steps

The method comprises the steps of extracting a vehicle image needing to be observed, extracting continuous frames of the vehicle image, and sequencing the vehicle image in a multi-frame mode in a time sequence.

9. The convolutional neural network-based license plate calibration recognition system of claim 8, wherein:

the license plate recognition module carries out recognition on the license plate numbers of the multi-frame images, and the process comprises the following steps:

setting a definition threshold value based on time sequence, removing the images which do not meet the requirements, and identifying the license plate in the images which meet the requirements; wherein the definition threshold can be adaptively adjusted according to human eyes; when the clear threshold is selected, the clear threshold is automatically set for the first time, and the clear threshold can be changed according to manual adjustment;

acquiring license plates in the images meeting the requirements by using a convolutional neural network to obtain a plurality of license plate identification results meeting the requirements;

the result comparison module is used for checking whether the license plate number identification results meeting the requirements are consistent or not, if not, pairwise pairing is carried out on any identification license plate number with the maximum similarity, and the identification license plate number is defined as an adjacent frame;

if the requirements are met, selecting two frames of images with close time as adjacent frames;

the method for comparing license plate number recognition results of adjacent frames to obtain a plurality of license plate consistency results comprises the following steps:

identifying the license plate numbers of the adjacent frame images respectively, identifying inconsistent positions if the license plate number results of the adjacent frames are inconsistent, and outputting respective results;

defining the symbol at the inconsistent position as a special symbol, marking the special symbol and inputting the special symbol into the networking information;

and the result determining module inquires the other same symbols except the special symbols in the networking information, and determines the final license plate number information by combining the contour information to obtain a license plate consistency result.

10. An electronic device comprising a convolutional neural network based license plate calibration recognition system as claimed in any one of claims 6-9.

Technical Field

The invention relates to the technical field of license plate recognition, in particular to a license plate calibration recognition method and system based on a convolutional neural network and an electronic device.

Background

The license plate recognition technology requires that the license plate of the moving automobile can be extracted and recognized from a complex background, and the information such as the license plate number and the color of the automobile can be recognized through the technologies such as license plate extraction, image preprocessing, feature extraction, license plate character recognition and the like.

In parking lot management, the license plate recognition technology is also a main means for recognizing the identity of a vehicle.

The license plate recognition technology is combined with an Electronic Toll Collection (ETC) system to recognize vehicles, and the vehicles can be automatically recognized and automatically charged without stopping when passing through a road junction. In the management of the parking lot, in order to improve the passing efficiency of vehicles at an entrance and an exit, the license plate recognition aims at the vehicles (such as a lunar truck and internal free passing vehicles) which do not need to collect parking fees, an unattended fast passage is built, the entrance and exit experience of card taking and non-stop is avoided, and the management mode of entering and exiting the parking lot is changed.

Due to the fact that the driving speeds of the automobiles are inconsistent, when the vehicles are subjected to license plate recognition of different vehicles, the images are often blurred when the vehicles are in a quick state, so that the correct license plate number cannot be recognized, and in addition, if the license plates in the continuous images are recognized to obtain different results, the existing technology cannot judge.

Disclosure of Invention

Aiming at the problems, the invention provides a license plate calibration and identification method and a license plate calibration and identification system based on a convolutional neural network, which can be used for carrying out clear selection on images when a vehicle is in a fast state, so that the optimal images can be obtained to identify the correct license plate number, and in addition, if different results are identified from the license plates in continuous images, the convolutional neural network is combined to carry out networking query on the contour information of the vehicle, so that the error of license plate identification can be effectively avoided, and the problems in the background technology can be effectively solved.

In order to achieve the purpose, the invention provides the following technical scheme: a license plate calibration and identification method based on a convolutional neural network is applied to electronic equipment and comprises the steps of

Acquiring vehicle contour information;

acquiring a multi-frame image of a certain vehicle, and identifying the license plate number of the multi-frame image;

comparing license plate number recognition results of adjacent frames to obtain a plurality of license plate consistency results;

and (4) checking a plurality of consistency results to determine the license plate information of a certain vehicle.

As a preferable technical solution of the present invention, the obtaining of the vehicle contour information includes obtaining the contour information of the vehicle, where the contour information includes

And processing the vehicle image by adopting a CNN network to obtain the color, the size and the type information of different vehicles.

As a preferred embodiment of the present invention, the method for acquiring a plurality of frames of images of a certain vehicle includes

The method comprises the steps of extracting a vehicle image needing to be observed, extracting continuous frames of the vehicle image, and sequencing the vehicle image in a multi-frame mode in a time sequence.

As a preferred technical solution of the present invention, the process of identifying the license plate number of the plurality of frame images includes:

setting a definition threshold value based on time sequence, removing the images which do not meet the requirements, and identifying the license plate in the images which meet the requirements; wherein the definition threshold can be adaptively adjusted according to human eyes; when the clear threshold is selected, the clear threshold is automatically set for the first time, and the clear threshold can be changed according to manual adjustment;

acquiring license plates in the images meeting the requirements by using a convolutional neural network to obtain a plurality of license plate identification results meeting the requirements;

checking whether the identification results of the license plates meeting the requirements are consistent or not, and if the identification results of the license plates do not meet the requirements, pairing any identification license plate with the maximum similarity in pairs to define adjacent frames;

and if the requirements are met, selecting two frames of images close in time as adjacent frames.

As a preferred technical solution of the present invention, the method for comparing license plate number recognition results of adjacent frames to obtain a plurality of license plate consistency results comprises:

identifying license plate numbers of adjacent frame images respectively, identifying inconsistent positions if license plate number results of adjacent frames are inconsistent, and outputting respective results;

defining the symbol at the inconsistent position as a special symbol, marking the special symbol, and inputting the special symbol into the networking information;

and inquiring other same symbols except the special symbols in the networking information, and determining final license plate number information by combining the contour information to obtain a license plate consistency result.

A license plate calibration and recognition system based on a convolutional neural network is applied to electronic equipment and comprises a vehicle positioning module, a vehicle identification module and a license plate recognition module, wherein the vehicle positioning module is used for positioning a vehicle needing license plate recognition and acquiring a clear outline;

the contour determining module is used for acquiring vehicle contour information;

the image acquisition module and the license plate recognition module are used for acquiring multi-frame images of a certain vehicle and recognizing the license plate number of the multi-frame images;

the result comparison module is used for comparing license plate number identification results of adjacent frames to obtain a plurality of license plate consistency results;

and the result determining module is used for checking a plurality of consistency results and determining the license plate information of a certain vehicle.

As a preferable technical solution of the present invention, the contour determining module acquires vehicle contour information, wherein the contour information includes

And processing the vehicle image by adopting a CNN network to obtain the color, the size and the type information of different vehicles.

As a preferred technical solution of the present invention, the method for acquiring the multi-frame image of a certain vehicle by the image acquisition module includes

The method comprises the steps of extracting a vehicle image needing to be observed, extracting continuous frames of the vehicle image, and sequencing the vehicle image in a multi-frame mode in a time sequence.

As a preferred technical solution of the present invention, the process of the license plate recognition module recognizing the license plate number of the multiple frames of images includes:

setting a definition threshold value based on time sequence, removing the images which do not meet the requirements, and identifying the license plate in the images which meet the requirements; wherein the definition threshold can be adaptively adjusted according to human eyes; when the clear threshold is selected, the clear threshold is automatically set for the first time, and the clear threshold can be changed according to manual adjustment;

acquiring license plates in the images meeting the requirements by using a convolutional neural network to obtain a plurality of license plate identification results meeting the requirements;

the result comparison module is used for checking whether the license plate number identification results meeting the requirements have consistency, and if the license plate number identification results do not meet the requirements, two pairs of the identification license plate numbers with the maximum similarity are carried out to define as adjacent frames;

if the requirements are met, selecting two frames of images with close time as adjacent frames;

the method for comparing license plate number recognition results of adjacent frames to obtain a plurality of license plate consistency results comprises the following steps:

identifying the license plate numbers of the adjacent frame images respectively, identifying inconsistent positions if the license plate number results of the adjacent frames are inconsistent, and outputting respective results;

defining the symbol at the inconsistent position as a special symbol, marking the special symbol, and inputting the special symbol into the networking information;

and the result determining module inquires the other same symbols except the special symbols in the networking information, and determines the final license plate number information by combining the contour information to obtain a license plate consistency result.

An electronic device comprising a convolutional neural network-based license plate calibration recognition system as described in any one of the above.

Compared with the prior art, the invention has the beneficial effects that:

comparing license plate number identification results of adjacent frames, identifying the license plate numbers of the images of the adjacent frames respectively, identifying inconsistent positions if the license plate number results of the adjacent frames are inconsistent, and outputting respective results; defining the symbol at the inconsistent position as a special symbol, marking the special symbol and inputting the special symbol into the networking information; the same symbols except for the special symbols are queried in the networking information, for example, if there is an adjacent frame image recognition result of 12345678 and 12346578 respectively, then the front 1234 and the rear two 78 are consistent, then the group of data can be represented as 1234 × 78, and 1234 × 78 is input into the networking information for viewing, and the final license plate number information is determined by combining the contour information of the vehicle which is initially recognized, so as to obtain a license plate consistency result. When the vehicle is in a fast condition, the image can be selected clearly, so that the optimal image is obtained to identify the correct license plate number, and in addition, if different results are identified from the license plates in the continuous images, the network query is carried out on the contour information of the vehicle by combining the convolutional neural network, so that the error of license plate identification can be effectively avoided.

Drawings

FIG. 1 is a schematic flow diagram of the process of the present invention;

FIG. 2 is a schematic flow chart illustrating the process of recognizing the license plate number of a plurality of frames of images according to the method of the present invention;

FIG. 3 is a schematic flow chart of a plurality of license plate consistency results obtained by the method of the present invention;

FIG. 4 is a schematic diagram of the general system of the present invention;

FIG. 5 is a schematic view of the wheel contour recognition of the present invention;

FIG. 6 is a schematic diagram of the convolution process of the present invention;

fig. 7 is a schematic diagram of CNN network identification numbers according to the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.